In this section, we introduce a discriminative sequence-to-sequence model for task-oriented dialogue. The traditional sequence of steps needed to produce a system turn in a task-directed dialogue is shown in
Figure REF , with an example from MultiWOZ 2.0 [1]}. Given a dialogue context containing previous user and system utterances, the dialogue system first predicts a belief state, consisting of a set of slot-value pairs (e.g. destination: Cambridge), to capture user intent. To ground the system with external information, the belief state can be converted into a database query in order to retrieve relevant information, such as the number of matches and booking information. Next, the system predicts a set of dialogue acts, representing the abstract meaning of the proposed dialogue response [2]}. Finally, a delexicalized dialogue response is generated, where slot values are replaced by generic placeholders, such as value_time for a train departure time, in order to reduce lexical variation. The delexicalized response can be converted to a lexicalized response in post-processing by filling in the slot values based on belief states and database information.
